scholarly journals Border security and surveillance System using IoT

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Low Cost ◽  
Cost Effective ◽  
Surveillance Systems ◽  
Security Assessment ◽  
Efficient System ◽  
Human Gestures ◽  
Set Up ◽  
International Border ◽  
Border Surveillance

Security along the international border is a critical process in security assessment; It must be exercised the 24x7. With the advancements in wireless IoT technology, it has become much easier to design, develop and deploy a cost-effective, automatic and efficient system for intrusion detection in the context of surveillance. This paper set up to set up the most efficient surveillance solution, we propose a Border Surveillance Systems and sensitive sites. this surveillance and security system is to detect and track intruders trespassing into the monitoring area along the border, it able which triggers off precocious alerts and valuation necessary for the catch of efficient measurements in case of a threat. Our system is based on the classification of the human gestures drawn from videos envoy by Drones equipped with cameras and sensors in real-time. All accomplished experimentation and acquired results showed the benefit diverted from the use of our system and therefore it enables our soldiers to watch the borders at each and every moment to effectively and at low cost.

The Benefit and Importance of Mobile Satellite Signal in Northern Nigeria: GPS Approach

2021 ◽  
Vol 02 (02) ◽  
Author(s):  
Ibrahim Abba ◽  
◽  
Salisu Muhammad ◽  
Lawan Bashir D. Bala ◽  
Emmanuel Joseph ◽  
...  
Keyword(s):  
Open Space ◽  
Low Cost ◽  
Research Work ◽  
Cost Effective ◽  
Signal Propagation ◽  
Space Environment ◽  
Gps Receiver ◽  
Satellite Signal ◽  
Mobile Satellite ◽  
Set Up

Lack of equipment to study mobile satellites signal propagation in colleges and universities prone this research work. A Handheld GPS receiver used as a tool for training college students to learn mobile satellite signal propagation using Global Positioning System (GPS) approach. These refer to the experimental setup of the equipment that is the connection done between the GPS receiver with a computer. The satellite propagation data received from the GPS machine can be recorded continuously with an updates rate of 2 seconds. The experiment was carried out in an open space environment at predetermine locations using simple setup, where a cheap, readily and available portable GPS receiver were connected to the computer to acquire propagation data. The computer was equipped with a self-developed package graphical user interface (GUI) monitoring the propagation information from the GPS satellites and saving the data. The developed system can be set up anywhere at any location. The sate-up will serve as a database for satellites view and analysis of mobile satellite data orbiting the sky of Northern part of Nigeria. Cost effective referring to a low-cost and readily available GPS receiver that can be easily set-up as compared to equipment designed specifically for an experimental purpose that is normally very expensive.

scholarly journals An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Francy Shu ◽  
Jeff Shu
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Low Cost ◽  
Fall Detection ◽  
Detection Methods ◽  
Surveillance Systems ◽  
Nursing Facilities ◽  
Risk Zones ◽  
Set Up ◽  
Current Detection

AbstractFalls are a leading cause of unintentional injuries and can result in devastating disabilities and fatalities when left undetected and not treated in time. Current detection methods have one or more of the following problems: frequent battery replacements, wearer discomfort, high costs, complicated setup, furniture occlusion, and intensive computation. In fact, all non-wearable methods fail to detect falls beyond ten meters. Here, we design a house-wide fall detection system capable of detecting stumbling, slipping, fainting, and various other types of falls at 60 m and beyond, including through transparent glasses, screens, and rain. By analyzing the fall pattern using machine learning and crafted rules via a local, low-cost single-board computer, true falls can be differentiated from daily activities and monitored through conventionally available surveillance systems. Either a multi-camera setup in one room or single cameras installed at high altitudes can avoid occlusion. This system’s flexibility enables a wide-coverage set-up, ensuring safety in senior homes, rehab centers, and nursing facilities. It can also be configured into high-precision and high-recall application to capture every single fall in high-risk zones.

scholarly journals Low-cost photolithography system for cell biology labs

2021 ◽  
Vol 7 (2) ◽  
pp. 550-553
Author(s):  
Benjamin K. Naggay ◽  
Kerstin Frey ◽  
Markus Schneider ◽  
Kiriaki Athanasopulu ◽  
Günter Lorenz ◽  
...  
Keyword(s):  
Light Source ◽  
Cell Biology ◽  
Soft Lithography ◽  
Uv Light ◽  
Low Cost ◽  
Cost Effective ◽  
Working Environment ◽  
Light Emitting ◽  
Set Up ◽  
High Level

Abstract Soft lithography, a tool widely applied in biology and life sciences with numerous applications, uses the soft molding of photolithography-generated master structures by polymers. The central part of a photolithography set-up is a mask-aligner mostly based on a high-pressure mercury lamp as an ultraviolet (UV) light source. This type of light source requires a high level of maintenance and shows a decreasing intensity over its lifetime, influencing the lithography outcome. In this paper, we present a low-cost, bench-top photolithography tool based on ninety-eight 375 nm light-emitting diodes (LEDs). With approx. 10 W, our presented lithography set-up requires only a fraction of the energy of a conventional lamp, the LEDs have a guaranteed lifetime of 1000 h, which becomes noticeable by at least 2.5 to 15 times more exposure cycles compared to a standard light source and with costs less than 850 C it is very affordable. Such a set-up is not only attractive to small academic and industrial fabrication facilities who want to enable work with the technology of photolithography and cannot afford a conventional set-up, but also microfluidic teaching laboratories and microfluidic research and development laboratories, in general, could benefit from this cost-effective alternative. With our self-built photolithography system, we were able to produce structures from 6 μm to 50 μm in height and 10 μm to 200 μm in width. As an optional feature, we present a scaled-down laminar flow hood to enable a dust-free working environment for the photolithography process.

scholarly journals A Cost-Effective and Efficient Electronic Design for Photovoltaic Systems for Solar Hot Water Production

2021 ◽  
Vol 13 (18) ◽  
pp. 10270
Author(s):  
Luis Cámara-Díaz ◽  
José Ramírez-Faz ◽  
Rafael López-Luque ◽  
Francisco José Casares
Keyword(s):  
Fossil Fuels ◽  
Electronic Device ◽  
Low Cost ◽  
Cost Effective ◽  
Electronic System ◽  
Hot Water ◽  
Photovoltaic System ◽  
Heating Process ◽  
Water Tank ◽  
Set Up

A significant percentage of energy consumption in buildings is to produce hot water. Photovoltaic solar heating can be considered a clean and renewable energy option—easy to install, silent, and without maintenance—to replace the consumption of fossil fuels used in this process. This paper presents a study that simulates the heating process using thermal electrical resistors powered by photovoltaic solar energy. For this purpose, a solar hot water installation has been set up. This installation consists of a water tank with an electric resistance connected to photovoltaic modules by means of a low-cost experimental electronic conversion system. This electronic system has been developed to avoid the need for inverters or batteries, typical of traditional photovoltaic solar installations. It is an isolated system since it is not connected to the power grid. The photovoltaic solar modules, the tank, and its heating resistance correspond to commercial models. This electronic system has a 95.06% yield, and it operates across the whole irradiance’s daily curve, having verified its operation over several months. Even though this is an experimental electronic device, it is financially viable as the cost of its components is below EUR 60 per kW peak capacity. The results obtained in a proper functioning system are promising, demonstrating the technical feasibility and economic advantages of using this type of isolated photovoltaic system to power heating processes.

scholarly journals Assessment of Electricity-Free Hydroponics in India: A Proof of Concept Field Study

2017 ◽  
Vol 10 (1) ◽  
pp. 45
Author(s):  
Indira Roy ◽  
Yelena Naumova ◽  
A. J. Both
Keyword(s):  
Smallholder Farmers ◽  
Low Cost ◽  
Cost Effective ◽  
Dry Mass ◽  
Chili Pepper ◽  
Production Costs ◽  
Effective Option ◽  
Deccan Plateau ◽  
Set Up ◽  
The Cost

Subsistence and smallholder farmers in the Deccan plateau region of India struggle with a predominantly hot and dry climate and often accumulated debt due to the cost of fertilizer that they need to increase yields for profitability. While a low-cost deep-flow technique hydroponic growing system (DFT) as a supplement to soil-based agriculture could help reduce debt, the cost of electricity needed to operate the DFT makes it inaccessible to these farmers. The objective of this project was to test the viability of electricity-free DFT which would substantially reduce production costs. Two DFT systems were set up in a shade net house and prepared with identical nutrients to grow chili pepper seedlings. Each DFT system was oxygenated for 30 minutes per day, one system using an electrical air pump, and the other system was oxygenated manually. After four weeks of growth, the dry mass of the shoots of the chili pepper seedlings in each system was measured. While the pump-oxygenated DFT system produced more dry matter, the manually-oxygenated system produced a larger number of visually healthier plants. Therefore, we conclude that electricity-free DFT hydroponics may be a viable alternative to pump-oxygenated DFT hydroponics, making hydroponic farming a cost-effective option for poor farmers.

scholarly journals Transcriptome annotation in the cloud: complexity, best practices, and cost

GigaScience ◽  
2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Roberto Vera Alvarez ◽  
Leonardo Mariño-Ramírez ◽  
David Landsman
Keyword(s):  
Computational Biology ◽  
Low Cost ◽  
Cost Effective ◽  
Disk Drive ◽  
Cloud Provider ◽  
Biological Databases ◽  
Cloud Platform ◽  
Sequence Alignments ◽  
Cloud Providers ◽  
Set Up

Abstract Background The NIH Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability (STRIDES) initiative provides NIH-funded researchers cost-effective access to commercial cloud providers, such as Amazon Web Services (AWS) and Google Cloud Platform (GCP). These cloud providers represent an alternative for the execution of large computational biology experiments like transcriptome annotation, which is a complex analytical process that requires the interrogation of multiple biological databases with several advanced computational tools. The core components of annotation pipelines published since 2012 are BLAST sequence alignments using annotated databases of both nucleotide or protein sequences almost exclusively with networked on-premises compute systems. Findings We compare multiple BLAST sequence alignments using AWS and GCP. We prepared several Jupyter Notebooks with all the code required to submit computing jobs to the batch system on each cloud provider. We consider the consequence of the number of query transcripts in input files and the effect on cost and processing time. We tested compute instances with 16, 32, and 64 vCPUs on each cloud provider. Four classes of timing results were collected: the total run time, the time for transferring the BLAST databases to the instance local solid-state disk drive, the time to execute the CWL script, and the time for the creation, set-up, and release of an instance. This study aims to establish an estimate of the cost and compute time needed for the execution of multiple BLAST runs in a cloud environment. Conclusions We demonstrate that public cloud providers are a practical alternative for the execution of advanced computational biology experiments at low cost. Using our cloud recipes, the BLAST alignments required to annotate a transcriptome with ∼500,000 transcripts can be processed in <2 hours with a compute cost of ∼$200–$250. In our opinion, for BLAST-based workflows, the choice of cloud platform is not dependent on the workflow but, rather, on the specific details and requirements of the cloud provider. These choices include the accessibility for institutional use, the technical knowledge required for effective use of the platform services, and the availability of open source frameworks such as APIs to deploy the workflow.

scholarly journals A comparative evaluation of a dye-based and probe-based RT-qPCR assay for the screening of SARS-CoV-2 using individual and pooled-sample testing

2020 ◽  
Author(s):  
Claudio Verdugo ◽  
Anita Plaza ◽  
Gerardo Acosta-Jamett ◽  
Natalia Castro ◽  
Josefina Gutiérrez ◽  
...  
Keyword(s):  
Low Cost ◽  
Rna Extraction ◽  
Cost Effective ◽  
Clinical Samples ◽  
Analytical Sensitivity ◽  
Detection Rates ◽  
Efficient System ◽  
Population Sampling ◽  
Effective Interventions ◽  
Surveillance Programs

ABSTRACTEffective interventions are mandatory to control the transmission and spread of SARS-CoV-2, a highly contagious virus causing devastating effects worldwide. Cost-effective approaches are pivotal tools required to increase the detection rates and escalate further in massive surveillance programs, especially in countries with limited resources that most of the efforts have focused on symptomatic cases only. Here, we compared the performance of the RT-qPCR using an intercalating dye with the probe-based assay. Then, we tested and compared these two RT-qPCR chemistries in different pooling systems: after RNA extraction (post-RNA extraction) and before RNA extraction (pre-RNA extraction) optimizing by pool size and template volume. We evaluated these approaches in 610 clinical samples. Our results show that the dye-based technique has a high analytical sensitivity similar to the probe-based detection assay used worldwide. Further, this assay may also be applicable in testing by pool systems post-RNA extraction up to 20 samples. However, the most efficient system for massive surveillance, the pre-RNA extraction pooling approach, was obtained with the probe-based assay in test up to 10 samples adding 13.5 µL of RNA template. The low cost and the potential use in pre-RNA extraction pool systems, place of this assays as a valuable resource for scalable sampling to larger populations. Implementing a pool system for population sampling results in an important savings of laboratory resources and time, which are two key factors during an epidemic outbreak. Using the pooling approaches evaluated here, we are confident that it can be used as a valid alternative assay for the detection of SARS-CoV-2 in human samples.

scholarly journals An Optical Scattering Based Cost-Effective Approach Towards Quantitative Assessment Of Turbidity And Particle Size Estimation In Drinking Water Using Image Analysis

2020 ◽  
Author(s):  
Soumendra Singh ◽  
Animesh Halder ◽  
Amrita Banerjee ◽  
Nur Hasan ◽  
Arpan Bera ◽  
...  
Keyword(s):  
Water Quality ◽  
Limit Of Detection ◽  
Low Cost ◽  
Cost Effective ◽  
Average Diameter ◽  
Optical Scattering ◽  
Size Estimation ◽  
Scattering Medium ◽  
Gold Standard Method ◽  
Set Up

Contaminated water consumption primarily for drinking purposes is the cause of approximately 502,000 global deaths every year mostly in economically challenging countries indicating the need for a cheap, easy to use a yet robust and scientifically proven method for determination of water quality. In this work, we have characterized the water quality utilizing the principles of optical scattering by the suspended particulate matter using a low-cost wireless-enabled camera. The images grabbed by the camera on an optically lit cast screen on a red and a blue dot were allowed to arrive through a “model scattering medium". An estimate of the amount of light reaching the detector camera essentially provide Optical Density of the medium. Edge blurring of the captured images reveals information of the suspended particulates (sizes) in the medium. The individual pixel information was analyzed and the 'edge blurring' phenomenon was shown on an RGB intensity curve. The average diameter of the dominant suspended particles presents in the model scattering medium is also estimated from the fitting parameters and compared with that from commercially available Dynamic Light Scattering (DLS) instrument. The system is effective in measuring bacterial growth and the acquired data have been compared with that of the growth curve obtained from the gold standard method. Limit of Detection (LOD) of the set-up was found to be 48 ppm. The extremely cost-effective nature of the set-up, the innovative method of analysis, and easy availability of components would expectedly make water quality assessment very easy and user friendly.

scholarly journals A Visual Aquaculture System Using a Cloud-Based Autonomous Drones

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 109
Author(s):  
Naomi A. Ubina ◽  
Shyi-Chyi Cheng ◽  
Hung-Yuan Chen ◽  
Chin-Chun Chang ◽  
Hsun-Yu Lan
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Cost Effective ◽  
Cloud Services ◽  
Fish Length ◽  
Surveillance Systems ◽  
Feeding Intensity ◽  
Fish Feeding ◽  
Intelligent Decision Making ◽  
Intelligent Decision

This paper presents a low-cost and cloud-based autonomous drone system to survey and monitor aquaculture sites. We incorporated artificial intelligence (AI) services using computer vision and combined various deep learning recognition models to achieve scalability and added functionality, in order to perform aquaculture surveillance tasks. The recognition model is embedded in the aquaculture cloud, to analyze images and videos captured by the autonomous drone. The recognition models detect people, cages, and ship vessels at the aquaculture site. The inclusion of AI functions for face recognition, fish counting, fish length estimation and fish feeding intensity provides intelligent decision making. For the fish feeding intensity assessment, the large amount of data in the aquaculture cloud can be an input for analysis using the AI feeding system to optimize farmer production and income. The autonomous drone and aquaculture cloud services are cost-effective and an alternative to expensive surveillance systems and multiple fixed-camera installations. The aquaculture cloud enables the drone to execute its surveillance task more efficiently with an increased navigation time. The mobile drone navigation app is capable of sending surveillance alerts and reports to users. Our multifeatured surveillance system, with the integration of deep-learning models, yielded high-accuracy results.

scholarly journals An Agile AI and IoT-Augmented Smart Farming: A Cost-Effective Cognitive Weather Station

Agriculture ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 35
Author(s):  
Amine Faid ◽  
Mohamed Sadik ◽  
Essaid Sabir
Keyword(s):  
Internet Of Things ◽  
Human Life ◽  
Low Cost ◽  
Cost Effective ◽  
Base Station ◽  
Ease Of Use ◽  
Environmental Data ◽  
Radio Communication ◽  
Smart Farming ◽  
Set Up

Internet of Things (IoT) can be seen as the electricity of 21st century. It has been reshaping human life daily during the last decade, with various applications in several critical domains such as agriculture. Smart farming is a real-world application in which Internet of Things (IoT) technologies like agro-weather stations can have a direct impact on humans by enhancing crop quality, supporting sustainable agriculture, and eventually generating steady growth. Meanwhile, most agro-weather solutions are neither customized nor affordable for small farmers within developing countries. Furthermore, due to the outdoor challenges, it is often a challenge to develop and deploy low-cost yet robust systems. Robustness, which is determined by several factors, including energy consumption, portability, interoperability, and system’s ease of use. In this paper, we present an agile AI-Powered IoT-based low-cost platform for cognitive monitoring for smart farming. The hybrid Multi-Agent and the fully containerized system continuously surveys multiple agriculture parameters such as temperature, humidity, and pressure to provide end-users with real-time environmental data and AI-based forecasts. The surveyed data is ensured through several heterogeneous nodes deployed within the base station and in the open sensing area. The collected data is transmitted to the local server for pre-processing and the cloud server for backup. The system backbone communication is based on heterogeneous protocols such as MQTT, NRF24L01, and WiFi for radio communication. We also set up a user-friendly web-based graphical user interface (GUI) to support different user profiles. The overall platform design follows an agile approach to be easy to deploy, accessible to maintain, and continuously modernized.

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